Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
Data Structures and Algorithms
2023-05-24 v1 Machine Learning
Abstract
We show that a simple single-pass semi-streaming variant of the Pivot algorithm for Correlation Clustering gives a (3 + {\epsilon})-approximation using O(n/{\epsilon}) words of memory. This is a slight improvement over the recent results of Cambus, Kuhn, Lindy, Pai, and Uitto, who gave a (3 + {\epsilon})-approximation using O(n log n) words of memory, and Behnezhad, Charikar, Ma, and Tan, who gave a 5-approximation using O(n) words of memory. One of the main contributions of this paper is that both the algorithm and its analysis are very simple, and also the algorithm is easy to implement.
Cite
@article{arxiv.2305.13560,
title = {Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!},
author = {Sayak Chakrabarty and Konstantin Makarychev},
journal= {arXiv preprint arXiv:2305.13560},
year = {2023}
}